1. Field of the Invention
The present invention relates generally to iterative interference cancellation in received wireless communication signals and, more particularly, to cancellation of interference in a MIMO-OFDM system.
2. Discussion of the Related Art
In an exemplary wireless multiple-access system, a communication resource is divided into separable subchannels, and a set of such subchannels is allocated to different users. For example, subchannels may include time slots, frequency slots, multiple-access codes, spatio-temporal subchannels, or any combination thereof. A plurality of subchannel signals received by a wireless terminal (e.g., a subscriber unit or a base station) may carry symbols for different users or may carry symbols for a single user.
If a single transmitter broadcasts different messages to different receivers, such as a base station in a wireless communication system broadcasting to a plurality of mobile terminals, the channel resource is subdivided in order to distinguish between messages intended for each mobile. Thus, each mobile terminal, by knowing its allocated subchannel(s), may decode messages intended for it from the superposition of received signals. Similarly, a base station typically separates signals it receives into subchannels in order to differentiate between users.
In a multipath environment, received signals are superpositions of time delayed (and complex scaled) versions of the transmitted signals. Multipath generally introduces interference both across a transmitted information signal (inter-symbol interference) and across adjacent transmitted signals (multiple-access interference). This effect can be viewed in the frequency domain as a frequency dependent attenuation, termed frequency selective fading. The level and phase of the fading are generally approximately constant across narrow bands of frequency, the maximum width of which is termed the coherence bandwidth of the channel.
When orthogonal frequency division multiplexing (OFDM) is employed as a transmission scheme, the available frequency band is divided into several subbands. If the subband spacing is sufficiently small relative to the coherence bandwidth characterizing the carrier frequency and propagation environment it is possible to treat each subband as a frequency-nonselective fading channel. This allows, among other things, for a relatively simple per tone equalizer to be employed and for efficient mapping of access rates to each subband. Furthermore, the different subcarriers can be assigned to different users to allow for interference-free transmission through OFDM-multiple access (OFDMA).
When multiple transmit and/or receive antennas are employed at the communication devices, the channel becomes a multiple-input multiple-output (MIMO) channel, and the extra degrees of freedom may be exploited to derive a rate increase (multiplexing gain) and/or an increase in redundancy (diversity gain) to allow for a higher reliability in the data transmission. These benefits are gained at the expense of an increase in the transmitter and the receiver complexity as the signals from the various transmit antennas generally mix in the physical channel. A transmitter side approach to mitigating this phenomenon is to weight the transmitter signals so that they add constructively at the receive antenna array (termed beamforming). A receiver approach is to employ a receiver matched to the propagation conditions to separate the information transmitted across the transmit array. Oftentimes, a combination of these approaches is employed.
The use of the linear minimum mean squared error (LMMSE) criterion for MIMO detection is a well-established technique. The mean squared error is measured between the transmitted data and the output of an LMMSIE processor. The mean squared error is generally a function of the spectral characteristics of the multipath fading channel and the multiplexing signal set employed for a user (i.e. the carrier(s) allocated for that user). When frequency domain spreading is employed in the transmission, the linear model may become quite large in dimensionality, as high as the product MNP where M transmit antennas are employed to transmit data across P subcarriers to a receiver employing N receive antennas. In such a case, a suboptimal approach would be to independently perform an LMMSE front end on each subcarrier, followed by a single dispreading operation.
The use of an efficient receiver architecture, such as the iterative interference canceller detailed in TCOM-IIC patent application, circumvents the complexity of performing the full matrix inverse required by the LMMSE approach. When mixed decisions on the transmitted data are employed within the cancellation loops, a further performance improvement may be realized over the LMMSE receiver.
In order to circumvent the need for the matrix inverse inherent in the LMMSE receiver, various soft iterative interference-cancellation techniques have been studied. The term soft cancellation is used in reference to a receiver that does not exploit information about the finite size of user constellations. Soft interference cancellers are motivated by well-known techniques of quadratic minimization that employ serial and/or parallel iterations.
In a MIMO system, the constellation employed at each layer of transmission may be known (in a single user space-time system) and/or estimated (when multi-user spatial multiplexing is employed). In such cases, it is possible to employ nonlinear parallel or serial interference cancellation (PIG or SIC, respectively), wherein hard decisions can be made on sufficiently reliable user symbols with the hard decisions coming from the symbol constellation. Such a mixed-PIG approach can produce a significant performance improvement over the optimal LMMSE receiver. Furthermore, when the transmitted data is further spread across several frequency bands through the use of a linear transformation at the transmitter, linear receivers such as the LMMSE are known to suffer performance degradations over nonlinear approaches. In particular they cannot achieve the diversity gains available through the frequency spreading.
Interleaving of the coded bits prior to modulation can buy back some of this performance, but requires very long interleaver depths to achieve the required diversity gain in frequency. A nonlinear receiver, such as the mixed decision PIG approach, may improve performance in such cases by exploiting additional diversity available at the modulated data level.